Knowledge Engineering and Data Science (KEDS) pISSN 2597-4602 

Vol 1, No 1, January 2018, pp. 20–25 eISSN 2597-4637 
 

 

 

 

 

https://doi.org/10.17977/um017v1i12018p20-25  

©2018 Knowledge Engineering and Data Science | W : http://journal2.um.ac.id/index.php/keds | E : keds.journal@um.ac.id 

This is an open access article under the CC BY-SA license (https://creativecommons.org/licenses/by-sa/4.0/) 

Market Basket Analysis to Identify Customer Behaviors  

by Way of Transaction Data 

Fachrul Kurniawan a, 1, *, Binti Umayah a, Jihad Hammad c,  

Supeno Mardi Susiki Nugroho b, Mochammad Hariadi b 

a Dept. of Informatics Eng., Maulana Malik Ibrahim State Islamic Univ., Jl. Gajayana No.50, Malang 65144, Indonesia 
b Dept. of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Jl. Raya ITS,Surabaya 60111, Indonesia 

c ICT Faculty, AlQuds Open University, Beit Jalla-The Main road-Khallat Al Badd, Bethlehem, Palestine 

1 fachrulk@ti.uin-malang.ac.id* 

* corresponding author 

 

 

I. Introduction 

Consumer behavior is a consumer activity in deciding to purchase, use, as well as consume the 
purchased goods and services including in the customer factors which can give a rise to their decisions 
whether to purchase and use products. Every customers have different needs and inclinations as well 
as have different behaviors in fulfilling those things. However, in the event of different behaviors to 
fulfill their needs, they still share some similarities, one of them is desiring to maximize their 
satisfactions in consuming a necessary product or service. 

Of that consumption activity, that can be inferred as to the behavior, pattern, or habit that the 
customers do in fulfilling their needs and desires. That behavior can be identified by way of the 
logging carried out by the intermediary provider of consumer needs (supermarket). The logging is 
brought off in view of the requisite of documentation and history data identification over the carrying 
out of transaction activity. 

In these recent years, transaction data have been commonly used as research and analysis objects 
for researchers. In this study, also, transaction data are to be re-processed/re-explored to generate more 
valuable information. For instance, information of an item whose sales is the highest. Besides, 
information can be utilized in regard with the stock addition of that item. Moreover, from transaction 
data there can be utilized as to the relation of each purchased item inside the customer’ basket. By that 
information, we can make use of it for effective product display/assortment to attract customers’ 
interest. The commonly-used application to analyze transaction data customers’ shopping basket is 
market basket analysis. 

Market basket analysis is one of the modes from data mining technique prevalently employed to 
analyze items/goods in one or more shopping baskets that a customer has in one particular moment 

ARTICLE INFO A B S T R A C T   

Article history: 

Received 30 August 2017 

Revised 13 September 2017 

Accepted 1 October 2017 

Published online 8 January 2018 

 

Transaction data is a set of recording data result in connections with sales-purchase 
activities at a particular company. In these recent years, transaction data have been 
prevalently used as research objects in means of discovering new information. One of 
the possible attempts is to design an application that can be used to analyze the existing 
transaction data. That application has the quality of market basket analysis. In 
addition, the application is designed to be desktop-based whose components are able 
to process as well as re-log the existing transaction data. The used method in designing 
this application is by way of following the existing steps on data mining technique. 
The trial result showed that the development and the implementation of market basket 
analysis application through association rule method using apriori algorithm could 
work well. With the means of confidence value of 46.69% and support value of 1.78%, 
and the amount of the generated rule was 30 rules. 

This is an open access article under the CC BY-SA license 

(https://creativecommons.org/licenses/by-sa/4.0/). 

Keywords: 

Data mining 

Market basket analysis 

Association rule 

Apriori 

 

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https://doi.org/10.17977/um017v1i12018p20-25
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21 F. Kurniawan et al. / Knowledge Engineering and Data Science 2018, 1 (1): 20–25 

[1]. Market basket analysis application ought to be designed and implemented at a supermarket not 
only owing to being able to help the sales promotion design but also able to be made as a reference to 
re-manage item stock’ incoming and outcoming in the warehouse. In this study, market analysis 
application will be implemented at BC UIN Malang supermarket, in regard with its inability to use 
transaction data. This application is expected to work well and is able to generate the desired result. 

A. Market Basket Analysis 

Market Basket Analysis is an analysis mode performed on customer behavior whilst shopping at a 
supermarket through the means identifying association and connections among various items placed 
by the customers in their shopping baskets [2]. In specific, Market Basket Analysis aims at 
simultaneously identifying the most frequently-purchased items by customers. Here, item is depicted 
as several kinds of products in supermarket. 

Using market basket analysis mode, a knowledge of what are the items oftenly purchased by the 
customers simultaneously and having an opportunity to be promoted can be obtained. With regards to 
the objective of market basket analysis mode to decide which products that customers purchase at the 
same time, whereby the name of this mode is taken from the behavior of the customers in placing 
shopping products into their shopping baskets or shopping list. Over identifying shopping basket 
pattern of a customer will significantly be able to help a company in using that information in respect 
of business strategy needs, one of them is placing the most frequently-purchased products 
simultaneously into one specific area. 

B. Association Rule 

Association rule is related to the statement of “what’ with what”. This matter can be in a form of 
statement on transaction activity carried out by the customers at a supermarket. From that statement, 
there has a strong relation to the study of customer transaction data database to determine the habit of 
a purchased product with what product, thus, association rule is frequently referred as market basket 
analysis [1], [2]. 

The significance of an associative rule can be figured in the presence of two parameters, namely 
support and confidence. Support (supporting value) is the percentage of combinations of product items 
in the database. While confidence (certainty value) is a value to determine the strength of inter-item 
relationships in association rules. 

𝑆 =  
∑(𝑇𝑎+𝑇𝑐)

∑(𝑇)
 (1) 

𝑆𝑢𝑝𝑝𝑜𝑟𝑡 (𝐴) =
𝑇ℎ𝑒 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑇𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛 𝑡ℎ𝑎𝑡 𝐶𝑜𝑛𝑡𝑎𝑖𝑛𝑠 𝐴

𝑇𝑜𝑡𝑎𝑙 𝑇𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛
 (2) 

where: 

S   = Support 

∑(𝑇𝑎 + 𝑇𝑐) = the number of transaction that contains antecendent and consequent. 

∑(𝑇) = the number of transaction. 

∁ =  
∑(𝑇𝑎+𝑇𝑐)

∑(𝑇𝑎)
 (3) 

𝐶𝑜𝑛𝑓𝑖𝑑𝑒𝑛𝑐𝑒 =  𝑃(𝐴 | 𝐵)  =  
𝑇ℎ𝑒 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑇𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛 𝑡ℎ𝑎𝑡 𝐶𝑜𝑛𝑡𝑎𝑖𝑛𝑠 𝐴 𝑎𝑛𝑑 𝐵

𝑇𝑜𝑡𝑎𝑙 𝑇𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛 𝑡ℎ𝑎𝑡 𝐶𝑜𝑛𝑡𝑎𝑖𝑛𝑠 𝐴
 (4) 

where: 

C   = Confidence 

∑(𝑇𝑎 + 𝑇𝑐) = the number of transaction that contains antecendent and consequent. 

∑(𝑇𝑎) = the number of transaction that contains antecendent. 

C. Customer Behaviour 

Definition of customer behavior is a dynamic interaction between cognition, affection, behavior, 
and its environment whereby someone performs exchange activities in their regular basis [3]. In view 
of this statement, there are three significant matters to grasp, namely: 



 F. Kurniawan et al. / Knowledge Engineering and Data Science 2018, 1 (1): 20–25 22 

1. Customer behavior bears dynamic characteristic, thus, hard to predict. 
2. Involving interaction, like cognition, affection, behavior, and the occurrences around 

customers, 
3. Involving exchange, like the exchange of item and money from merchant to customer 

Four factors that could give a rise to customer purchase in shopping [4], some of them were: 

1. Cultural Factor 
2. Social Factor 
3. Personal Factor 
4. Psychological Factor 

There were three variables that must be regarded in understanding customer behavior, namely 
stimulus variable, response variable, and intervening variable[5]. 

D. Data Mining 

The researcher argued that Data Mining had garnered the interest of information industry and 
public during in that recent years, this was brought about by massive data availability and the 
necessities to render information data to usable knowledge [6], [7]. 

Data Mining is prevalently depicted as retrieval pattern on a large set of crude data so that some 
unveiled knowledge in the data can be found. Data Mining is considered a major step in the process 
of knowledge retrieval in the database. Data Mining inherits many aspects and techniques from 
established fields of science first. Data Mining also has long roots from fields such as artificial 
intelligence (Artificial Intelligent), machine learning, statistics, database and also information 
retrieval. 

II. Methods 

In association rule mode, the implemented algorithm in the making of market basket analysis 
application is Apriori algorithm. Algoirthm is used to develop frequent itemset using 1-item first, then 
value support of every item would be later counted. Item whose support value was above the minimum 
support value was selected as 1-itemset high frequency pattern and as 2-itemset candidates. By that of 
1-itemset, the development of frequent itemset into 2-itemset which would then the value of 
confidence be calculated next was recursively brought off. 

III. Results and Discussion 

This application was implemented in private supermarket of UIN Malang Business Center, over 
inputting the collected transaction data of which the data used were transaction data in BC UIN 
Malang on October 1st, 2014 with 1553 collected data. In additions of the stored transaction receipts 
of 890753 up to 891319 transaction receipts. 

The further step was inputting minimum support and minimum value of confidence. In illustration, 
user wanted to insert transaction data with receipts from 890753 up to 890853. From that receipt there 
were 56 transaction data with 20 items could be retrieved. Those 56 transaction data were obtained 
from the transaction receipts whose item amounts were minimumly 2 in 1 single transaction 

The trial system was carried out in means of system trial of market basket analysis application by 
way of association rule counting mode using apriori algorithm. In this study, a discussion of the tests 
that had been carried out on the system and evaluation of the results responded by the system. From 
this test, how patterns differences of the generated data would be known. The test was performed on 
56 data by entering the initial value 

 Minimum support= 3 
 Minimum confidence= 10 

Fig. 2 shows the data that would be used for the test, which were the receipts between 890753 up 
to 890853 with the number of transaction data was 56 data assorted in respect of “Receipts” 

The calculation of the support value of 1 item can be obtained through the following formula: 

𝑆𝑢𝑝𝑝𝑜𝑟𝑡 (𝐴) =
𝑇ℎ𝑒 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑇𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛 𝑤ℎ𝑖𝑐ℎ 𝑐𝑜𝑛𝑡𝑎𝑖𝑛𝑠 𝐴

𝑇𝑜𝑡𝑎𝑙 𝑇𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛
 (5) 



23 F. Kurniawan et al. / Knowledge Engineering and Data Science 2018, 1 (1): 20–25 

While the support value for 2 items can be obtained with the following formula: 

𝑆𝑢𝑝𝑝𝑜𝑟𝑡(𝐴, 𝐵) =
𝑇𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛 𝑁𝑢𝑚𝑏𝑒𝑟 𝑊ℎ𝑖𝑐ℎ 𝐶𝑜𝑛𝑡𝑎𝑖𝑛𝑠 𝐴 𝑎𝑛𝑑 𝐵

𝑇𝑜𝑡𝑎𝑙 𝑇𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛
  

Confidence value calculation was generated of this following formula: 

𝐶𝑜𝑛𝑓𝑖𝑑𝑒𝑛𝑐𝑒 = 𝑃(𝐴|𝐵) =
𝑇ℎ𝑒 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑇𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛 𝑊ℎ𝑖𝑐ℎ 𝐶𝑜𝑛𝑡𝑎𝑖𝑛𝑠 𝐴 𝑎𝑛𝑑 𝐵

𝑇𝑜𝑡𝑎𝑙 𝑇𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛 𝑤ℎ𝑖𝑐ℎ 𝑐𝑜𝑛𝑡𝑎𝑖𝑛𝑠 𝐴
  

 

Fig. 1. Apriori algorithm design 

 

 

Fig. 2. Application implementation 



 F. Kurniawan et al. / Knowledge Engineering and Data Science 2018, 1 (1): 20–25 24 

In calculating support 1-itemset with 2-itemset, the inputted minimum support was differentiated. 
Minimum support for 2-itemset calculation was deducted as if it was evened out, there would be no 
candidate for 2-itemset. Hence, the generated output was only constrained o 1-itemset. Thereby, on 
the test of this study, for the calculation of 2-itemset, the researcher deliberate entered different 
minimum support value with minimum support value on 1-itemset calculation by means of generating 
the desired recommendation outcome. 

 

Fig. 3. The calculation result of 1 item support value 

 

 

Fig. 4. Confidence value calculation result 



25 F. Kurniawan et al. / Knowledge Engineering and Data Science 2018, 1 (1): 20–25 

IV. Conclusions 

In the test and analysis that have been carried through, it is concluded that of development and 
implementation of market basket analysis over association rule mode using apriori algorithm on the 
transaction data of Business Centre (BC) UIN Malang supermarket, can perform well. With the 
confidence value average of 46.69% from support value 1.78% and the generated rules are 30 rules. 
The data transaction rule/ pattern which was retrieved had a low association tendency. That was due 
to the owned data and the analyzed data did not really support association analyses/ the relation 
between items. 

References 

[1] M. Kaur and S. Kang, “Market Basket Analysis: Identify the Changing Trends of Market Data Using Association Rule 
Mining,” Procedia Comput. Sci., vol. 85, no. Cms, pp. 78–85, 2016. 

[2] A. Mansur and T. Kuncoro, “Product Inventory Predictions at Small Medium Enterprise Using Market Basket Analysis 
Approach-Neural Networks,” Procedia Econ. Financ., vol. 4, no. Icsmed, pp. 312–320, 2012. 

[3] X. Su, “Intertemporal Pricing with Strategic Customer Behavior,” Manage. Sci., vol. 53, no. 5, pp. 726–741, 2007. 

[4] G. Armstrong, S. Adam, S. Denize, and P. Kotler, Armstrong, G., Adam, S., Denize, S., & Kotler, P. Pearson Australia., 
2014. 

[5] E. Sherman, A. Mathur, and R. B. Smith, “Store Environment and Consumer Purchase Behavior: Mediating Role of 
Consumer Emotions,” Psychol. Mark., vol. 14, no. 4, pp. 361–378, 1997. 

[6] N. Jothi, N. A. Rashid, and W. Husain, “Data Mining in Healthcare - A Review,” Procedia Comput. Sci., vol. 72, pp. 
306–313, 2015. 

[7] A. Bertoni and T. Larsson, “ScienceDirect Data Mining in Product Service Systems Design: Literature Review and 
Research Questions,” Procedia CIRP, vol. 64, pp. 306–311, 2017. 

 

https://doi.org/10.1016/j.procs.2016.05.180
https://doi.org/10.1016/j.procs.2016.05.180
https://doi.org/10.1016/s2212-5671(12)00346-2
https://doi.org/10.1016/s2212-5671(12)00346-2
https://doi.org/10.1287/mnsc.1060.0667
https://doi.org/10.1002/(sici)1520-6793(199707)14:4%3C361::aid-mar4%3E3.0.co;2-7
https://doi.org/10.1002/(sici)1520-6793(199707)14:4%3C361::aid-mar4%3E3.0.co;2-7
https://doi.org/10.1016/j.procs.2015.12.145
https://doi.org/10.1016/j.procs.2015.12.145
https://doi.org/10.1016/j.procir.2017.03.131
https://doi.org/10.1016/j.procir.2017.03.131